1. Field of the Invention
This invention relates to systems for discriminating among multiple signals to recover information and, more specifically, to an adaptive system for recovering a signal from among several signal sources in a channel having reverberation.
2. Discussion of the Related Art
The separation of independent sources from an array of sensors is a classic but difficult problem in signal processing. Generally, the signal sources as well as their mixture characteristics are unknown. Without knowledge of the signal sources other than the general assumption of source independence, the signal processing problem is denominated "blind separation of sources". The separation is "blind" because nothing is known about the frequency or phase of the independent signals.
A concrete example of the blind separation of sources problem is where the pure (source) signals are sounds generated in a room and the mixed (sensor) signals are the outputs of several microphones (FIG. 1). Each of the pure signals is delayed and attenuated in some manner during transmission from source to microphone, where it is then mixed with other delayed and attenuated source signals. Multipath signals ("ghosts" created by reverberation) are delayed versions of the source signals arriving from different directions. This is denominated the "cocktail party" problem, where a person wishes to listen to a single sound source while filtering out other interfering sources including those created by reverberation.
Practitioners in the signal processing arts have pursued solutions to the blind source separation problem because of their broad application in many fields. For instance, in underwater acoustic digital communication, a receiver must eliminate multipath or reverberating versions of the transmitted signal to avoid unacceptable levels of intersymbol interference. The same multipath distortion problem is also well-known in the cellular telecommunications art.
Because the human ear automatically performs blind source separation, some practitioners have explored the neural network art for solutions to the blind separation of sources problem. For instance, Christian Jutten, et al ("Space or Time Adaptive Signal Processing By Neural Network Models", Neural Networks for Computing, Snowbird, UI, J. S. Denker, Ed., AIP Conference Proceedings 151, pp. 207-211, 1986) first introduced a simple neural network, herein denominated the Herault-Jutten (HJ) network, with adaptive separation capability.
Since its introduction, the HJ network has been extensively studied by practitioners in the art. For a detailed discussion of the HJ network, reference is made to C. Jutten, et al, "Blind Separation of Sources, Part I: An Adaptive Algorithm Based On A Neuromimetic Architecture", Signal Processing 24(1), pp. 1-10 (1991). Jutten, et al show that their HJ network can provide an exact solution to the blind source separation problem provided that the signal mixtures are linear and that the number of independent sensors is at least equal to the number of sources. Unfortunately, it is not commonly possible to obtain N distinct linear combinations of N signals without delays or phase shifts. This is especially the case in channels with reverberation. To generate N full-rank linear combinations of inputs for the HJ network, microphones must be placed at N different locations for signal sources located at N different places (FIG. 1). The propagating medium between the N sources and the N sensors produces different weights on the different source signal arrivals at each sensor and introduces significant signal delays that cannot be accommodated by the conventional HJ network.
For a statistical explanation of the HJ network function and a discussion of a non-adaptive version of the HJ network, reference is made to Pierre Comon, et al "Blind Separation of Sources, Part II: Problem Statement", Signal Processing 24(1), pp. 11-20 (1991). Comon, et al observe that the HJ network actually functions by searching for common zeros of N functionals through pipelined stochastic iterations. They show that it relies on the assumed independence of sensor signals, which follows from the assumption of independent source signals only if the sensor signals are linear combinations of the source signals. They observe that any introduction of non-linearity changes the problem to one requiring solution of an overdetermined system of non-linear equations with several variables; a class of very difficult problems.
For a discussion of the stability of the HJ network, reference is made to E. Sorouchyari, "Blind Separation Sources, Part III: Stability Analysis", Signal Processing 24(1), pp. 21-29 (1991). Sorouchyari shows that using simple linear and cubic HJ network adaptation functions f(x) and g(x) offers convergence and stability that cannot be improved through the use of higher order non-linear adaptation functions.
For an extensive discussion of a monolithic circuit implementation of the HJ network and a review of HJ network operation, reference is made to Marc H. Cohen, et al, "Analog VLSI Implementation of An Auto-Adaptive Network for Real Time Separation of Independent Signals", Advances in Neural Information Processing Systems, Vol. 4, Morgan-Kaufmann, San Mateo, Calif. (1992).
Because of the difficulty of ensuring linear combinations of source signals at the sensor outputs, the problem of separating non-linear signal combinations is of great interest. For instance, John G. Proakis ("Adaptive Equalization Techniques For Acoustic Telemetry Channels", IEEE Journal of Oceanic Engineering, Vol. 16, No. 1, pp. 21-31, Jan. 1991) provides a tutorial review of adaptive equalization techniques for reducing intersymbol interference in high-speed digital communications over time-dispersive channels. Also, Jeffrey H. Fischer, et al ("A High Data Rate, Underwater Acoustic Data-Communications Transceiver", IEEE Oceans 92, Vol. 2, pp. 571-576, 1992) describe an underwater high-speed data communications transceiver that features direct-sequence spread-spectrum encoding to mitigate intersymbol interference arising from reverberation in shallow acoustic channels. Neither Proakis nor Fischer, et al propose multichannel blind separation as a model for reducing intersymbol interference. Both practitioners rely on frequency redundancy in the signals; the classical adaptive techniques they advocate either treat multipath signals as noise or rely on embedded training signals and enormous computational complexity. Except for the nonlinearity rising from time delays over the source-to-sensor path, the HJ network offers a superior and simpler solution to the intersymbol interference problem.
Accordingly, John C. Platt, et al ("Networks For The Separation of Sources That Are Superimposed and Delayed", Advances in Neural Information Processing Systems, Vol. 4, Morgan-Kaufmann, San Mateo, Calif., 1992) have proposed extending the HJ network to also estimate a matrix of time delays while estimating the HJ network mixing matrix. Platt, et al have proposed a new network to separate signals that are mixed either with time delays or through filtering. They show that the Herault-Jutten learning rules fulfill a minimum output power principle, which they then apply to their extension. However, Platt, et al also note that their learning technique has multiple stable states and they cannot predict convergence to a solution except through experimentation.
Accordingly, a reliable and useful method for coping with unknown delays in the blind separation of sources problem is a clearly-felt need in the art. The related unresolved problems and deficiencies are clearly felt in the art and are solved by this invention in the manner described below.